Cart
Create Account
Sign In
Collection:
This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more.
Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications? power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores.
The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.
- IEEE MemberUS $495.00
- Society MemberUS $495.00
- IEEE Student MemberUS $495.00
- Non-IEEE MemberUS $695.00
Videos in this product
Instruction-Level Parallelization
This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more. Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications? power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores. The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.
Vector Computation
This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more. Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications? power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores. The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.
Automatic Parallelization
This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more. Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications? power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores. The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.
Multigrain Parallelization and Power Reduction
This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more. Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications? power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores. The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.
Vectorization
This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more. Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications? power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores. The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.
Vectorization/Parallelization in the Intel Compiler
Peng Tu is a Principle Engineer and manages the Technology Pathfinding engineering team in the Developer Product Division of Intel Corporation. Previously, he had also managed Intel Compiler's IA32/Intel64 global optimizer and code generation team. His teams developed Intel's C++ Extension of Array Notation and the Intel SPMD Compiler (ISCP). Peng has a PhD in Computer Science from University of Illinois at Urbana-Champaign in 1995. Prior to Intel, he worked on various compiler products at SGI and Tensilica Inc.
Dynamic Parallelization
Prof. Rudolf Eigenman (School of Electrical and Computer Engineering, Purdue University) examines how to optimize compilers, programming methodologies and tools, performance evaluation for high-performance computers, and cyber-infrastructures. He currently serves as Program Director at the US National Science Foundation.
Dependences and Dependence Analysis
Prof. Utpal Banerjee (University of California, Irvine) has published four books on loop transformations and dependence analysis, with a fifth one on instruction-level parallelism on the way. He has run international computer conferences and is a co-editor of the International Journal of Parallel Programming. He has a PhD in pure mathematics from Carnegie-Mellon University, a PhD in computer science from the University of Illinois, Urbana-Champaign, as well as an MSc in applied mathematics and a post MSc diploma in nuclear physics from Calcutta University.
Autoparallelization for GPUs
Prof. Wen-Mei Hwu (University of Illinois at Urbana-Champaign) holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His research interests are in the area of architecture, implementation, compilation, and algorithms for parallel computing. He is the chief scientist of Parallel Computing Institute and director of the IMPACT research group (www.impact.crhc.illinois.edu). He is a co-founder and CTO of MulticoreWare. Hwu serves as one of the principal investigators of the NSF Blue Waters Petascale computer project. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley.
Vectorization/Parallelization in the IBM Compiler
Yaoqing Gao is a senior technical staff member at IBM Canada Lab. His major interests are compilation technology, optimization and performance-tuning tools, parallel programming models and languages, and computer architecture. He has been doing research and development for IBM XL C/C++ and Fortran compiler products on IBM POWER, System z, CELL processors, and Blue Gene. He is an IBM Master inventor and has authored more than 30 issued and pending patents. Before joining IBM, Dr. Gao conducted research on parallel and distributed processing and programming languages at Tsinghua University, the National University of Singapore, the University of Tokyo, and the University of Alberta.
Round Table
All participant round table
The Polyhedral Model
Paul Feautrier is now an emeritus professor at the Ecole Normale Sup?rieure de Lyon. He has been one of the prime movers behind the polyhedral model, an abstract representation of regular programs. Initially devised for automatic parallelization, this model is now used for program analysis and verification, code generation and many other topics.